Kernel dies while computing persistence

[MarioAuditore]

I tried to play around with covariance matrices from geomstats:

import gudhi as gd  
import geomstats.datasets.utils as data_utils

data, patient_ids, labels = data_utils.load_connectomes()

dgm = gd.RipsComplex(
    distance_matrix = data[0],
    max_edge_length = 1.0
).create_simplex_tree(max_dimension = 2)

dgm.compute_persistence()

However this code just instantly kills the jupyter kernel. Python 3.11.4 on MacOS, M1 pro

[DavidLapous]

Hi Mario, from what I'm looking at, your data[0] looks like a correlation matrix, which does not look like distance matrix ---I think the segfault comes from here. --> I agree that Gudhi should handle that better. A solution can be to give a distance between points given by 1-data[0] instead, i.e., points that are close together in terms of correlation will be connected sooner.

[mglisse]

Indeed. The code will only look at the (strict) lower triangular matrix, but you have negative values in this matrix, so we end up with edges that have a filtration value smaller than that of the vertices (0), which violates the definition of a filtration. We could add some tests to avoid crashing the Python interpreter. Testing if a SimplexTree is a proper filtration may be a bit expensive though.